Research on Transmission Line Small Target Detection and Defect Recognition Based on Machine Vision

Yanjun Dong, Zhonghong Ou, Xiaoyu Yin, Xin Lu, Tao Yao

2023

Abstract

At present, the unmanned aerial vehicle (UAV) is faced with two difficulties in the course of inspection of power transmission line: (1) it is difficult to find small targets. The existing machine vision methods have poor performance in the detection of small targets, and there are still deficiencies in multi-scale feature extraction and fusion. (2) because of the uneven distribution of defect sets and normal sets, the differences based on the classification of semantic information, and the fusion of shallow location features and deep semantic features, it is difficult to identify and classify defects effectively.

Paper Citation

in Harvard Style

Dong Y., Ou Z., Yin X., Lu X. and Yao T. (2023). Research on Transmission Line Small Target Detection and Defect Recognition Based on Machine Vision. In Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT; ISBN 978-989-758-677-4, SciTePress, pages 271-274. DOI: 10.5220/0012280600003807

in Bibtex Style

@conference{anit23,
author={Yanjun Dong and Zhonghong Ou and Xiaoyu Yin and Xin Lu and Tao Yao},
title={Research on Transmission Line Small Target Detection and Defect Recognition Based on Machine Vision},
booktitle={Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT},
year={2023},
pages={271-274},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012280600003807},
isbn={978-989-758-677-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT
TI - Research on Transmission Line Small Target Detection and Defect Recognition Based on Machine Vision
SN - 978-989-758-677-4
AU - Dong Y.
AU - Ou Z.
AU - Yin X.
AU - Lu X.
AU - Yao T.
PY - 2023
SP - 271
EP - 274
DO - 10.5220/0012280600003807
PB - SciTePress